Popular Bank is a renowned financial institution recognized for its commitment to innovation and excellence in banking. The bank has a diverse range of services tailored to meet the needs of individuals and businesses, making it a cornerstone in the financial industry.

For those aspiring to join Popular Bank as a Data Scientist, the role demands a high level of expertise in data analysis, statistical modeling, and machine learning. Data Scientists at Popular Bank are expected to leverage vast datasets to uncover actionable insights, drive strategic decisions, and contribute to the advancement of the bank’s technology landscape.

In this guide, we’ll walk you through the interview process at Popular Bank, share some commonly asked Data Scientist interview questions, and provide essential tips to help you excel. Let’s dive in!

Submitting Your Application

The first step in securing a Data Scientist position at Popular Bank is to submit a compelling application that reflects your technical skills and keen interest in joining the company. Whether you were contacted by a Popular Bank recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Popular Bank Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Popular Bank data scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Popular Bank data scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Popular Bank’s data systems, ETL pipelines, and SQL queries.

In the case of data scientist roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Popular Bank office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data scientist role at Popular Bank.

Quick Tips For Popular Bank Data Scientist Interviews

• Understand The Financial Space: Popular Bank values candidates who are well-versed in the financial sector. Make sure to familiarize yourself with industry-specific terminologies and challenges.
• Strong Analytical Skills: Popular Bank’s data science interviews assess how well you can provide business-driving insights with data science. Brush up on your knowledge of quantitative analysis, statistics, and machine learning models.
• Cultural Fit: Demonstrate how you would fit in with Popular Bank's core values. Highlight experiences that portray your collaborative nature and innovative thinking.

Typically, interviews at Popular Bank vary by role and team, but commonly Data Scientist interviews follow a fairly standardized process across these question topics.

• What are the Z and t-tests, and when should you use each? Explain the purpose and differences between Z and t-tests. Describe scenarios where one test is preferred over the other.

• How would you reformat student test score data for better analysis? Given two datasets of student test scores, identify drawbacks in their current format. Suggest formatting changes and discuss common issues in "messy" datasets.

• What metrics would you use to evaluate the value of marketing channels? Given data on marketing channels and costs for a B2B analytics dashboard company, identify key metrics to determine the value of each marketing channel.

• How would you determine the next partner card using customer spending data? With access to customer spending data, outline a method to identify the best partner for a new credit card offering.

• How would you investigate the impact of a redesigned email campaign on conversion rates? Analyze whether a new email journey led to an increase in conversion rates or if other factors contributed. Consider historical data and other potential influences.

• Write a function search_list to check if a target value is in a linked list. Write a function, search_list, that returns a boolean indicating if the target value is in the linked_list or not. You receive the head of the linked list, which is a dictionary with keys value and next. If the linked list is empty, you'll receive None.

• Write a query to find users who placed less than 3 orders or ordered less than $500 worth of product. Write a query to identify the names of users who placed less than 3 orders or ordered less than $500 worth of product. Use the transactions, users, and products tables.

• Create a function digit_accumulator to sum every digit in a string representing a floating-point number. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

• Develop a function to parse the most frequent words used in poems. You're hired by a literary newspaper to parse the most frequent words used in poems. Poems are given as a list of strings called sentences. Return a dictionary of the frequency that words are used in the poem, processed as lowercase.

• Write a function rectangle_overlap to determine if two rectangles overlap. You are given two rectangles a and b each defined by four ordered pairs denoting their corners on the x, y plane. Write a function rectangle_overlap to determine whether or not they overlap. Return True if so, and False otherwise.

• How would you design a function to detect anomalies in univariate and bivariate datasets? If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

• What are the drawbacks of the given student test score datasets, and how would you reformat them? Assume you have data on student test scores in two layouts (dataset 1 and dataset 2). What are the drawbacks of these layouts? What formatting changes would you make for better analysis? Describe common problems in “messy” datasets.

• What is the expected churn rate in March for customers who bought subscriptions since January 1st? You noticed that 10% of customers who bought subscriptions in January 2020 canceled before February 1st. Assuming uniform new customer acquisition and a 20% month-over-month decrease in churn, what is the expected churn rate in March for all customers who bought the product since January 1st?

• How would you explain a p-value to a non-technical person? How would you explain what a p-value is to someone who is not technical?

• What are Z and t-tests, and when should you use each? What are the Z and t-tests? What are they used for? What is the difference between them? When should you use one over the other?

• How does random forest generate the forest and why use it over logistic regression? Explain the process of how random forest generates multiple decision trees to form a forest. Discuss the advantages of using random forest over logistic regression, such as handling non-linear data and reducing overfitting.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where bagging is preferred over boosting and vice versa. Provide examples of the tradeoffs between the two methods.

• What kind of model did the co-worker develop for loan approval? Identify the type of model used for determining loan approval based on customer inputs. Explain how to measure the difference between two credit risk models over time, considering personal loans are paid in monthly installments. List metrics to track the success of the new model.

• What’s the difference between Lasso and Ridge Regression? Describe the differences between Lasso and Ridge Regression, focusing on their regularization techniques and how they handle feature selection and multicollinearity.

• What are the key differences between classification models and regression models? Outline the main differences between classification and regression models, including their objectives, output types, and common use cases.

What is the average salary for a Data Scientist at Popular Bank?

Q: What is the interview process at Popular Bank like?
The interview process at Popular Bank typically includes an initial screening with HR, followed by technical interviews that test your data science skills, coding abilities, and problem-solving techniques. Depending on the position, there may also be case studies or project-based assessments.

Q: What skills are required for the Data Scientist role at Popular Bank?
To excel as a Data Scientist at Popular Bank, you should have strong analytical skills, proficiency in programming languages like Python or R, experience with machine learning algorithms, and familiarity with data visualization tools. Strong communication and collaborative skills are also highly valued.

Q: What kind of projects can I expect to work on at Popular Bank?
As a Data Scientist at Popular Bank, you'll engage in projects that involve developing predictive models, analyzing customer data, optimizing financial processes, and contributing to strategic business decisions. You'll have the opportunity to work on cutting-edge technologies and impact the organization’s data-driven strategies.

Q: How can I prepare for an interview at Popular Bank?
To prepare for an interview at Popular Bank, it’s crucial to research the company and its data initiatives thoroughly. Practice common interview questions using Interview Query, and ensure you are well-versed in key data science concepts, coding skills, and statistical techniques. Be ready to demonstrate your problem-solving abilities and discuss your previous experiences in data science projects.

Q: What is the company culture like at Popular Bank?
Popular Bank fosters a collaborative and innovative culture that encourages continuous learning and professional growth. The company values diversity, inclusivity, and a strong sense of community among its employees, making it a supportive environment for career development.

Are you ready to embark on an exciting career journey with Popular Bank as a Data Scientist? Dive into our detailed Popular Bank Interview Guide where we’ve compiled crucial interview questions and insights to set you on the path to success. For an enriched preparation experience, explore our other interview guides like data analyst and software engineer to get a holistic view of Popular Bank’s interview process.

At Interview Query, we are committed to empowering you with the tools, confidence, and strategies needed to excel in every Popular Bank Data Scientist interview question and beyond. For more tailored resources across various companies, check out our extensive company interview guides.

Good luck with your interview!